Is there a linear relationship between the dose of ruminant<i>trans</i>-fatty acids and cardiovascular risk markers in healthy subjects: results from a systematic review and meta-regression of randomised clinical trials
Bibliographic record
Abstract
The effects of ruminant (R) trans-fatty acids (TFA) on the risk of CVD are still under debate. It could be argued that the lack of the effect of R-TFA may be the result of the small amount of their intake. Taking into consideration the growing available data from intervention studies, we carried out a systematic review and meta-regression to assess the impact of R-TFA intake levels on changes in the total cholesterol: HDL-cholesterol (TC:HDL-C) ratio. A systematic review of the literature was conducted and thirteen randomised clinical trials were included, yielding a total of twenty-three independent experimental groups of subjects. A univariate random-effects meta-regression approach was used to quantify the relationship between the dose of R-TFA and changes in the TC:HDL-C ratio. To consider several potential modifiers such as subject and dietary characteristics, a multivariate regression analysis was performed. We found no relationship between R-TFA intake levels of up to 4.19% of daily energy intake (EI) and changes in cardiovascular risk factors such as TC:HDL-C and LDL-cholesterol (LDL-C):HDL-C ratios. In addition, a multivariate regression analysis that included other dietary variables, as well as subject baseline characteristics, confirmed that doses of R-TFA did not significantly influence the changes in the lipid ratio. Our findings showed that doses of R-TFA did not influence the changes in the ratios of plasma TC:HDL-C and LDL-C:HDL-C. These data suggest that TFA from natural sources, at least at the current levels of intake and up to 4.19% EI, have no adverse effects on these key CVD risk markers in healthy people.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.049 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".